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# Copyright (c) 2016, Max Zwiessele, Alan Saul
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import unittest, numpy as np

class TestDebug(unittest.TestCase):
    def test_checkFinite(self):
        from GPy.util.debug import checkFinite
        array = np.random.normal(0, 1, 100).reshape(25,4)
        self.assertTrue(checkFinite(array, name='test'))
        
        array[np.random.binomial(1, .3, array.shape).astype(bool)] = np.nan
        self.assertFalse(checkFinite(array))

    def test_checkFullRank(self):
        from GPy.util.debug import checkFullRank
        from GPy.util.linalg import tdot
        array = np.random.normal(0, 1, 100).reshape(25,4)
        self.assertFalse(checkFullRank(tdot(array), name='test'))
        
        array = np.random.normal(0, 1, (25,25))
        self.assertTrue(checkFullRank(tdot(array)))
    
    def test_fixed_inputs_median(self):
        """ test fixed_inputs convenience function """
        from GPy.plotting.matplot_dep.util import fixed_inputs
        import GPy
        X = np.random.randn(10, 3)
        Y = np.sin(X) + np.random.randn(10, 3)*1e-3
        m = GPy.models.GPRegression(X, Y)
        fixed = fixed_inputs(m, [1], fix_routine='median', as_list=True, X_all=False)
        self.assertTrue((0, np.median(X[:,0])) in fixed)
        self.assertTrue((2, np.median(X[:,2])) in fixed)
        self.assertTrue(len([t for t in fixed if t[0] == 1]) == 0) # Unfixed input should not be in fixed

    def test_fixed_inputs_mean(self):
        from GPy.plotting.matplot_dep.util import fixed_inputs
        import GPy
        X = np.random.randn(10, 3)
        Y = np.sin(X) + np.random.randn(10, 3)*1e-3
        m = GPy.models.GPRegression(X, Y)
        fixed = fixed_inputs(m, [1], fix_routine='mean', as_list=True, X_all=False)
        self.assertTrue((0, np.mean(X[:,0])) in fixed)
        self.assertTrue((2, np.mean(X[:,2])) in fixed)
        self.assertTrue(len([t for t in fixed if t[0] == 1]) == 0) # Unfixed input should not be in fixed

    def test_fixed_inputs_zero(self):
        from GPy.plotting.matplot_dep.util import fixed_inputs
        import GPy
        X = np.random.randn(10, 3)
        Y = np.sin(X) + np.random.randn(10, 3)*1e-3
        m = GPy.models.GPRegression(X, Y)
        fixed = fixed_inputs(m, [1], fix_routine='zero', as_list=True, X_all=False)
        self.assertTrue((0, 0.0) in fixed)
        self.assertTrue((2, 0.0) in fixed)
        self.assertTrue(len([t for t in fixed if t[0] == 1]) == 0) # Unfixed input should not be in fixed

    def test_fixed_inputs_uncertain(self):
        from GPy.plotting.matplot_dep.util import fixed_inputs
        import GPy
        from GPy.core.parameterization.variational import NormalPosterior
        X_mu = np.random.randn(10, 3)
        X_var = np.random.randn(10, 3)
        X = NormalPosterior(X_mu, X_var)
        Y = np.sin(X_mu) + np.random.randn(10, 3)*1e-3
        m = GPy.models.BayesianGPLVM(Y, X=X_mu, X_variance=X_var, input_dim=3)
        fixed = fixed_inputs(m, [1], fix_routine='median', as_list=True, X_all=False)
        self.assertTrue((0, np.median(X.mean.values[:,0])) in fixed)
        self.assertTrue((2, np.median(X.mean.values[:,2])) in fixed)
        self.assertTrue(len([t for t in fixed if t[0] == 1]) == 0) # Unfixed input should not be in fixed